Method and apparatus for intelligent active and semi-active vibration control

ABSTRACT

A generalized minimum variance type of control operates on combined optimal and self-tuning control theorems, and is applicable to the design of active, semi-active, and hybrid vibration control systems. The system operates in a multiple-input/multiple-output manner, so when both noise and vibration are important, e.g. interior of a vehicle, the resulting nulling signal will be based on diminishing both vibrations and noise. The system operates by directly nulling primary vibrations, in an active mode, and/or by developing a variable bandwidth mechanical filter, in a semi-active mode, and applying nulling signals accordingly to the vibration source. Artificial intelligence is incorporated into the system to learn on-line the dynamics of the system, e.g. vehicle modal parameters. This intelligence is used to modify decision making in the system, based on results of past performance, without reprogramming or tuning of the system. The system incorporates digital electronic circuitry to convert acceleration and/or audio signals into proper format for the software logic residing in a microprocessor chip. Synchronizing signal is not needed. Hardware used includes a state of the art high power microprocessor, capable of handling sixteen sensory input signals and generating eight output signals. Controlling noise and vibration control in a vehicle, four audio signals from the driver, passenger, and back seat areas, and accelerometer signals from different seat tracks, the steering column, and the floor pan can be input to the microprocessor. Resulting control signals can be two for adjustable front engine mounts, and six for the adjustable body (cradle) mounts. Software resident in memory includes a first program to perform a modal extraction of vibration and/or noise from the sensors, to perform a minimum variance calculation, then to perform an intelligent control calculation based on recorded past performance and on fuzzy logic compensation. Output signals generated and applied to actuators minimize and/or filter the vibration or noise.

FIELD OF THE INVENTION

This invention relates to methods and apparatus for cancelling and/orminimizing vibrations, including the nulling of primary vibrations andthe cancellation of repetitive or random vibrations, using a controlsignal which is applied through a variety of actuation sources, e.g.electromagnetic, hydraulic, pneumatic, or materials which changecondition in response to some stimulus.

BACKGROUND OF THE INVENTION

One system, and variations thereof, for cancelling vibrations has beenproposed in which the vibration is sensed via an appropriate pick-up,and a cancellation signal is created which is a 180° phase shift of thesensed signal. The cancellation signal is applied to or near thevibration source, thereby cancelling or at least greatly attenuating thevibration source. U.S. patents disclosing such a system are U.S. Pat.Nos. 4,153,815, 4,417,098, 4,489,441, and 4,566,118.

Such systems are based on adaptive signal processing techniques whichmay result in an inherent instability which could amplify rather thanattenuate the vibration. Also, such systems require a direct measurementof a synchronizing signal which provides them directly with the value ofthe frequency of the excitation source (vibration). Those systemsoperate on the basis of synthesizing the vibration source signal,synchronizing it with the excitation signal, then delaying such signalto achieve a 180° phase difference and applying it to cancel the effectof the excitation source. This results in a high sensitivity andpotentially unstable vibration cancellation due to exact phasing needs.

U.S. Pat. Nos. 4,122,203; 4,153,815; 4,417,098; 4,490,841; 4,527,282;4,566,118; 4,600,863; and 4,654,871 disclose the work of G.B. Chaplin inthe area of repetitive phenomena. The active noise control systemsdisclosed therein are designed for one-dimensional systems, and allapproaches described require some type of "synchronizing" signal. Thus,those systems require some type of sensor linked directly to theexcitation source. Those systems cannot be used for random vibration ornoise sources. They require a relatively long processing time, and aredirected predominantly to acoustic systems, and do not appear to relateto active vibration control.

U.S. Pat. Nos. 4,473,906 and 4,562,589 (Warneka) disclose a departurefrom the systems disclosed in the aforementioned Chaplin patents,relating to use of a feedforward control signal. The techniquesdisclosed require a direct measurement from the noise or vibrationsource, after which this signal is inverted and used to cancel thedetected noise or vibration. Using a feedforward signal allowsattenuation of random excitations, but these systems again require adirect measurement from the source and in many applications there is nodirect access to the noise or vibration source.

U.S. Pat. Nos. 4,667,676 and 4,667,677 disclose an approach based onadaptive filters (LMS and RLMS) and feedback signals to estimate theexcitation source signal. The shortcomings of those systems are a)potential for instability, b) a need for a high degree of on-lineprocessing, and c) use of only feedback signals which limits thesystem's application to broadband noise and vibrations. The disclosedsystems have been mostly used in noise control systems.

U.S. Pat. Nos. 4,649,505 and 4,862,506 (Noise Control Technologies)appear to be based on hardware modifications of prior art, usingadaptive filters (such as introduced by Widrow; 1960's), and using a LMSalgorithm which does not guarantee controlled system stability.

Another system has been disclosed in which a source of vibration ismonitored (sensed) and an attenuating signal is applied to or near thesource. The attenuating signal is modified in opposition to changessensed at the vibration source, until the combination of the two resultsin cancellation of the vibration, or attenuation thereof to somepredetermined level. No phase-shifted attenuating signal is employed.Systems of this type are disclosed in a paper entitled "A Multiple ErrorLMS Algorithm and Its Application to the Active Control of Sound andVibration" by Stephen J. Elliott, Ian M. Strothers & Philip A. Nelson,IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.ASSP-33 No. 10, October 1987, and in a paper entitled "A Unified ControlStrategy for the Active Reduction of Sound and Vibration" by N. J.Doelman, Journal of Intelligent Material Systems and Structures, Vol. 2No. 4, pages 558-580, October 1991.

Further, previously proposed vibration control schemes require a fastFourier Transform (FFT) analyzer, which adds significantly to the costof the system and/or increases the amount of on-line computation.

SUMMARY OF THE INVENTION

The present invention is an improvement on the generalized minimumvariance (GMV) type of control such as exemplified by the Doelman paper.The invention operates on the basis of combined optimal and self-tuningcontrol theorems, and is applicable to the design of active,semi-active, and hybrid vibration control systems. This system operatesin a multiple-input/multiple-output (MIMO) manner. Therefore, when bothnoise and vibration are important, e.g. interior of a vehicle, then theresulting nulling signal will be based on minimizing, or at leastdiminishing, both vibrations and noise. The system, that is the methodand apparatus of the invention, operates by directly nulling primaryvibrations, in an active mode, and/or by developing a variable bandwidthmechanical filter, in a semi-active mode, and applying nulling signalsaccordingly to the vibration source. Artificial intelligence isincorporated into the system to learn automatically, on-line, thedynamics of the system, such as vehicle modal parameters, and thisintelligence is used to modify future decision making in the systems,based on results of past performance, without need for reprogramming ortuning of the system.

A vibration control system according to the invention incorporatesdigital electronic circuitry to convert acceleration and/or audio(microphone) signals into proper format for the software logic whichresides in a microprocessor chip. This technique does not require asynchronizing signal, and can be implemented on inexpensivemicroprocessors.

The hardware used in an actual embodiment of the invention takes fulladvantage of state of the art high power microprocessors. It is capableof handling sixteen sensory input signals and generating eight outputsignals. Therefore, in the case of a noise and vibration control in avehicle, it is possible to combine four audio (microphone) signals fromthe driver, passenger, and back seat areas, and accelerometer signalsfrom different seat tracks, the steering column, and the floor pan. Theresulting control signals to the vehicle can be two for adjustable frontengine mounts, and six for the adjustable body (cradle) mounts. Thus,this hardware can take full advantage of available sensory information,and develop nulling signals for several actuators simultaneously.

The software used by the microprocessor in the present invention causesthat processor to perform a number of steps (i.e. a cycle) according toprograms which are developed from a set of algorithims and are residentin the system memory. The first program causes the system to perform amodal extraction of vibration and/or noise from the sensors. This stepmay not require repetition at the beginning of each cyclic operation,unless the system senses that the previous result was out of range.However, the modal extraction is on-line (interactive) and alwaysavailable as part of the system over all performance. The next step isto perform a minimum variance calculation, then the following stepperforms an intelligent control calculation based on recorded pastperformance and on fuzzy logic compensation, to achieve parameteroptimization, then an output signal or signals are generated and appliedto appropriate actuators of the system to minimize and/or filter thevibration or noise introduced to the system from the structure beingmonitored and controlled.

Thus, the primary object of the invention is to provide a systemperceiving (looking at) the effects of vibration or noise, and seekingto determine the dominant frequency or frequencies in such effect, thenfunctioning to minimize that effect. This approach reduces cost andcomplexity of the system, and thus is more practical for an on-boardtype of control system as in a vehicle, as well as in other uses.

The on board microprocessor repeatedly runs the routines of the systemcycle when the vehicle (or other controlled apparatus) is active.Results (e.g. nulling signals) of each cycle are stored to provide asource of information for the intelligence routine, while the modalextraction routine can be omitted from the system cycle if results arewithin range.

Other objects and advantages of the invention will be apparent from thefollowing detailed description, the accompanying drawings, and theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of the general layout of a vibration andnoise control system constructed according to the invention and appliedto an automotive vehicle, and employing several sensors and actuatorscoupled to a multi-input, multi-output intelligent controller;

FIG. 2 shows a specific embodiment of the invention as applied to anautomotive installation and employing a multi-input/multi-out controllerusing several sensors and actuators;

FIGS. 3, 4 and 5 are diagrams showing the design of circuit boards,including components as identified, which are used in an on-boardvehicle installation such as shown in FIG. 2;

FIG. 6 is a diagram showing the relation of the central processing unitof the apparatus with various inputs and outputs, including resourcealgorithims, input and feedback devices, and output signals to actuatorsdriven by the system;

FIG. 7 is a diagram showing a modal representation of the system;

FIG. 8 is a diagram illustrating the extraction of structural modes fromthe system; and

FIG. 9 is a block diagram of the logic of the system of the invention;

FIG. 10 is a diagram of the system operation; and

FIG. 11 is a diagram of a suitable power supply circuit for the system.

DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 shows in schematic diagram a vibration control system constructedaccording to the invention, in which a vibration source 1 (which may bea vehicle engine, a portable generator, an appliance motor, etc.)generates vibration waves. In the general diagram (FIG. 1) 11 denotesthe structure to which the system is applied, and to which the vibrationis input. FIG. 2 is a diagram of a specific embodiment of the inventionfor control of noise and vibration in the compartment of a vehicle. Inboth these general and specific embodiments, a sensor 2 (typically anaccelerometer, a force transducer, a tachometer, a displacement sensor,a microphone) is located near source 1 to sense the vibration. There maybe multiple sensors of different types, as hereinafter explained withrespect to FIG. 2. The output of sensor 2 is fed into an analog signalconverter/conditioning module 3 (see FIG. 3 for board details). Ifsensor 2 produces an analog signal, such signal is converted intodigital form by module 3; if the sensor output is a digital signal, suchsignal is filtered by module 3 for aliasing.

The resulting digital output signal from module 3 is fed to the on-boardmemory 5 of a microcontroller 4, and resides in its appropriate memorybank with proper time index. Data sets stored in memory 5 are accessedby the central microprocessing unit 6 which comprises a CPU and acoprocessor capable of performing floating point arithmetic. It ispossible to substitute a digital signal processing chip (DSP) for theCPU and coprocessor of the processing unit. FIGS. 3, 4 and 5 showelectronic circuit boards and their components, which are per se known,used in conjunction with the microcontroller; their place in the systemis indicated in FIG. 1. The major control algorithms of the system arestored in a ROM section 7 of the microcontroller 4. Such algorithmsinclude (see FIG. 6) structural modal identification, optimal control,intelligent control, and optimal mount/suspension design algorithms inthe case of an automotive mount control system.

After data manipulation, the CPU will generate an appropriate controlsignal, which is sent through a calibration module 8, to deriveappropriate actuator signals depending upon whether an active orsemi-active vibration control is desired. The resulting digital outputsignal is transmitted to an output port 9, converted to an analogsignal, and then sent to one or more actuators 10. The active orsemi-active actuators 10 will drive the structure (e.g an adjustableengine mount EM (FIG. 2) or an adjustable suspension component, e.g.body mounts BM in FIG. 2) in such manner as to attenuate the vibration.A further sensor 12 provides a feedback signal to the microcontrollerunit 4.

By way of example, the control system can include three small electroniccircuit boards. The first board (see FIG. 3) houses the microprocessoror CPU, an on-board memory, a timer, and the operating systems. The CPUmay be an 80486SLC processor such as available from Cyrix or TexasInstruments, with a 25 MHz clock. There is 1 MB of on-board memory and a256 Kbyte flash EPROM, along with 512 Kbyte of static RAM.

FIG. 4 is a schematic diagram of the 12-bit analog to digital (A-D)board, including sixteen input channels with ability to convert 70,000samples per second. This board has three 16-bit counter/timers. Thereare nineteen digital I/O lines, arranged in three groups for differentcombinations of inputs (sensors accessed) and outputs (actuatorsreceiving control signals). The sixteen input channels are more thanwhat is typically required in an automotive system where both acousticsignals (e.g. microphones) and vibration signals (e.g. accelerometers)may be used.

The third electronic board in the control system is an eight channeldigital to analog (D-A) output board (see FIG. 5). The analog voltagesare generated on this board, thus only 5V is needed for DAC operations.In a typical large size vehicle, e.g. a light truck or van, two enginemounts and six body mounts may need to be controlled simultaneously,thus eight-channel DAC is provided for the controller.

To simplify the power requirements of the control device, it isdesirable to require only a positive power source. For example, in thecase of the active engine mounts EM or body mounts BM, the logicallyavailable power source is the vehicle battery which (at least in theU.S.A.) provides +12 V power with respect to ground. However, for anactive vibration control system, the control signal will have bothpositive and negative polarities. Therefore, power sources of ± V arerequired. To eliminate such requirement, an electronic circuit isprovided as shown in FIG. 11.

This circuit comprises an operational amplifier AMP with a resistorfeedback R₁, two transistors Q₁ and Q₂, and a loading resistor R₁. Thecontrol signal generated by the microprocessor is introduced to theinput of the operational amplifier. The power source, e.g. the vehiclebattery, is connected to the input port V_(s). Since the base terminalsof Q₁ and Q₂ are connected to the microprocessor signal by theoperational amplifier AMP, then Q₁ and Q₂ can function as a voltagesource or a voltage sink, respectively. Therefore, the output terminals+V_(cc) and -V_(cc) will provide both a positive source and a negativesource. This electronic circuitry can eliminate the need for positiveand negative voltage supplies.

In the case of a noise and vibration control in a vehicle, it ispossible to combine four audio (microphone) signals from the driver,passenger, and back seat areas microphones MIC, and accelerometersignals from different seat track accelerometers AST, the steeringcolumn accelerometer AS, and the floor pan. The resulting controlsignals to the vehicle can be two for adjustable front engine mounts EM,and six for the adjustable body (cradle) mounts BM. Thus, this hardwarecan take full advantage of available sensory information, and developnulling signals for several actuators simultaneously.

A program has been compiled and successfully operated a system asdisclosed, using Borland Turbo-C++ version 1.0 and an Octagon 486stand-alone controller unit (CPU), according to the algorithims setforth hereinafter.

MODE IDENTIFICATION

Whenever a vibration source (e.g. an engine of a vehicle, or a motor ofan electrical appliance) excites a structure such as the compartment(e.g. engine compartment or appliance housing) in which the source issupported, depending upon the frequency content of the excitationsignal, one or more modes of the structure (e.g. the vehicle driven bythe engine) will be in turn excited. The present invention includes amethod of identifying all of such excited modes on-line, then feedingthem to the controller and its algorithm.

A major departure of the present invention from prior art approaches anddevices is that the method and apparatus of this invention can providephysically realizable representation of the structure being monitoredand controlled. Prior art approaches assume a time series representationof a system in the form of an auto-regressive with exogenous (ARX)input, or an auto-regressive with moving average (ARMA) input, and withexternal input (ARMAX). Results of those approaches have no physicalsignificance and cannot be related to the modal characteristics of thestructure, e.g. automotive chassis bending mode or body flexural mode.Other mechanisms require a Fast Fourier Transform (FFT) analyzer todevelop modal characteristics.

The present approach eliminates the need for FFT. Further, the methodand apparatus of the present invention can easily handlemultiple-input/multiple-output (MIMO) systems, using the resulting modalcharacteristics.

For example, as shown schematically in FIG. 2, in a vehicle forcetransducers provide input signals from engine mounts EM, adjustable bodymounts BM, and noise input signals from microphones MIC, accelerometerAS from the steering column, and accelerometers AST from the seat tracksprovide error signals. When the method of this invention is appliedbetween the seat track and engine mount signals, a number of modes willbe identified on an on-line (or interactive) basis. These will relate tothe portion of the structure (e.g. the vehicle body) excited by enginevibrations. The modes identified from the engine mount signals and thesteering column signal may have the steering column mode morepredominant than those identified by the seat track signals or data.Thus the intelligent controller will use such interpretation to assureaccurate representation and physically correct identification of thevehicle vibration modes.

The software program consists of four routines, namely (1) modalextraction, (2) generalized minimum variance, and intelligence, based on(3) learning ability which records and utilizes past performanceinformation, and (4) fuzzy logic. A complete cycle of the system programuses all four routines in the aforesaid sequence. The first or modalextraction routine may be omitted from the cycle if the result ofrunning the previous cycle is not out of range of the system.

MODAL EXTRACTION

As mentioned above, the present invention provides a method to identifyall of the excited modes on-line, then feed them to the vibrationcontrol program which is stored according to the vibration controlalgorithm. This operation step may not need to be performed at thebeginning of every system cycle, but this step is necessary if it isdetermined the previous result was out of range.

FIG. 7 is a pictorial presentation of a vibrating exciting structurewhich is comprised of several modes. Each mode can be represented by atransfer function as ##EQU1## which in a discrete domain would be##EQU2## where ##EQU3## and T is the sampling rate. Thus every mode isrepresented by three parameters b₁, a₁, and a₂. If these parameters areidentified,then modal parameters can be obtained from the followingequations. ##EQU4##

Thus, from time domain data it is possible to extract structural modesas shown in FIG. 8. It is assumed that the structure consists of Nmodes, where N is initially unknown. So long as the feedback signalcontains modes, the modal extractor identifies new modes, operating inthe least square sense.

To prevent generation of an error sequence that is auto-correlated withnon-zero mean, a filter is added to the model, on the equation error.This filter, whose parameters are initially unknown, serves to bring theresidual bias to zero and to minimize its autocorrelation properties.Thus the system output, predicted by the composition of extracted modescan be represented by ##EQU5## where B/A represents those modes alreadyidentified, and the second term represents potential modes stillimbedded in the measurement signal, e.g. a seat track accelerometer. Ina simplified case, the above equation is put into the following form forinclusion in the modal extraction algorithm

    y(t)=b.sub.1 u(t-1)+b.sub.1 d.sub.1 u(t-2)+(c.sub.1 -d.sub.1)y(t-1)+a.sub.1 (c.sub.1 -d.sub.1)y(t-2)+a.sub.2 (c.sub.1 -d.sub.1)y(t-3)-(a.sub.1 +c.sub.1)y(t-1)-(a.sub.2 +a.sub.1 c.sub.1)y(t-2)-a.sub.2 c.sub.1 y(t-3)(6)

To find the unknown parameters, a_(i), b_(i), c_(i), and d_(i) aconstraint optimization problem in the following form is developed.##EQU6## Using constraint optimization methods, an algorithm isdeveloped to obtain θ and consequently the modal parameters of thestructure.

The resulting optimization problem becomes one of minimizing J(θ)subject to the inequality constraints, which translates into Kuhn-Tuckerconstraint qualifications. Namely, it results in the followingequations: ##EQU7## The vector form of the Newton-Raphson technique forsolving nonlinear equations is applied to obtain the system modalparameters. In the developed software, that is stored in the ROM, thelower-upper (LU) factorization technique is used for inverting theHassian matrix during iteration to avoid "divide by zero" problems.

Generalized Minimum Variance Optimal Control

The generalized minimum variance controller is especially suitable forthe problems of active vibration and active noise control. This form ofcontrol always tends to minimize variations of a given signal, e.g.acceleration of a steering column, from a nominal level of no vibrationor zero acceleration.

In every active noise or vibration control system, an estimation of theperformance signal, in the described example steering column vibration,is required in order to estimate the level of control effort that shouldbe introduced by the actuator, in this case an active engine mount.Therefore, some form of signal estimation or system identification isrequired. In general the following can be stated ##EQU8## wherey(t)=performance signal at time T

u(t-t₁)=control signal from actuator

t₁ =time delay caused by the propagation time from the actuator to theperformance signal sensor

ζ(t)=random input (e.g. road vibration)

A(q⁻¹)=1+a₁ q⁻¹ +a₂ q⁻² + . . . +a_(n) q^(-n)

q⁻¹ =backward shift operator, e.g. q⁻¹ x(t)=x(t-1)

The B(q⁻¹), C(q⁻¹) and D(q⁻¹) are also polynomials in terms of q⁻¹,similar to the A(q⁻¹). Depending whether all or some of these fourpolynomials are considered, the IIR, FIR, ARMA, or ARMAX representationof the system would be the result.

A major departure between the technique used in this invention and priorart systems is based on this signal or system representation. Asdescribed above, in accordance with the present invention all fourpolynomials exist whereas in prior art systems one or more of them areassumed to be zero. Also, in the present invention it is assumed ζ(t) isa Gaussian random signal, whereas prior art techniques may or notconsider ζ(t), and if considered it is assumed to be white noise.

In terms of mathematical derivations, the present invention has twomajor improvements over the prior art formulation, such as described inthe Doelman paper. First, instead of using an ARMA or ARMAX model of thestructure, the present system uses the modal representation that hasbeen developed for the structure. Also, due to hardware ability, theoptimal control structure of the present invention is based on aquadratic weighted sum of both acoustic (e.g. microphone signals) andvibration (e.g. accelerometer) signals.

In order to derive a minimum variance control, a cost function must bedefined. In general, it is desired to minimize the square of variationsover time, while restricting the total actuator power, namely

    Cost Function.tbd.J.tbd.E{[Py(t+τ.sub.1)+Qu(t)].sup.2 }(10)

where E[.]denotes the expected value of a stochastic signal, and P and Qare the weighting factors penalizing excessive variance on y orexcessive actuator input u(t). A special case of this cost function iswhen Q=0, i.e. no restriction is applied to the actuator input. Thus,the cost function

    J={[y(t+τ.sub.1)].sup.2 }                              (11)

is less attractive than the cost function defined above, because theformer provides the flexibility of trading off performance versuscontrol effort. For example, in the case of applying active vibrationcontrol to a vehicle, geometrical restrictions of the actuator in theform of constraint output power may be enforced. The cost function firststated above provides the ability to analytically determine the optimumactuator, whereas the special case cost function requires extensivetrial and error. This is another major difference between the presentinvention and prior art. The technique described herein provides ageneral solution, while previous techniques have developed special casesof methodology.

In application to motor driven vehicles, the present invention employsboth feedforward and feedback. The feedback action reduces effects ofrepetitive disturbances, e.g. engine vibration, whereas the feedforwardloop reduces the effect of random (broadband) disturbances, e.g.vibration due to irregular road inputs.

INCORPORATION OF THE LEARNING TECHNIQUES

In addition to the generalized version of the control technique providedby this invention, an additional novel feature is provided byincorporating learning ability into the system. This provides betterusage of sensory information available to the active or semi-activecontroller. This technique utilizes past performance to improve futureactions, and in addition, to simplify the amount of on-linecomputations, it applies fuzzy logic to compensate quantitatively forunexpected loads and/or disturbances. A few simple "if--then" rulesincorporate fuzzy logic into the system. Thus, the proposed controllerhas two levels of intelligence:

i. learning based on past performance ##EQU9## where T is the learningsample, and α₁ and α₂ are learning gains. Therefore, the control signalto an actuator (e.g. active engine mounts) will be improved through itssubsequent interactions, thus improving the system performancecontinuously.

ii. fuzzy logic compensation

    u(i)=u(i-T)+β.sub.1 y(i-1)+β.sub.2 [y(i-1)-y(i-2)](13)

where β₁ and β₂ are fuzzy compensator gains, derived from a small set ofrules that are fixed based on the level of activities of the describederror signals and their derivatives.

While the methods herein described, and the form of apparatus forcarrying these methods into effect, constitute preferred embodiments ofthis invention, it is to be understood that the invention is not limitedto these precise methods and form of apparatus, and that changes may bemade in either without departing from the scope of the invention, whichis defined in the appended claims.

What is claimed is:
 1. The method of controlling and attenuatingvibration or noise in a structure, such as an automotive vehicle, wherethe vibration is induced by vibration excitation sources,comprisingproviding a plurality of sensors for extracting signalsrelated to vibration and/or acoustic noise from the structure and itsenvirons, providing a plurality of actuators for applying attenuatingnoise and/or vibration to the structure, providing a microprocessorhaving inputs from the sensors and outputs to the actuators, performingan on-line modal extraction of vibration and/or noise from the sensors,then operating the microprocessor with an optimal control algorithmbased on minimum variance, then operating the microprocessor using anintelligent control algorithm, the microprocessor then generating anoutput signal and applying such signal to the actuators which willminimize and/or filter the vibration or noise introduced to thestructure.
 2. The method defined in claim 1, wherein the modalextraction step is performed on an as-needed basis which is determinedby whether the previous cycle of operation was in range or out of rangeof the desired operation for the system.
 3. The method defined in claim1, wherein the intelligence control step operates according to thealgorithms ##EQU10##
 4. The method as defined in claim 1 wherein theoptimal control step is performed according to the algorithm

    y(t)=b.sub.1 u(t-1)+b.sub.1 d.sub.1 u(t-2)+(c.sub.1 -d.sub.1)y(t-1)+a.sub.1 (c.sub.1 -d.sub.1)y(t-2)+a.sub.2 (c.sub.1 -d.sub.1)y(t-3)-(a.sub.1 +c.sub.1)y(t-1)-(a.sub.2 +a.sub.1 c.sub.1)y(t-2)-a.sub.2 c.sub.1 y(t-3)

where y(t)=performance signal at time T u(t-t₁)=control signal fromactuator t₁ =time delay caused by the propagation time from the actuatorto the performance signal sensor ζ(t)=random input (e.g. road vibration)A(q⁻¹)=1+a₁ q⁻¹ +a₂ q⁻² + . . . +a_(n) q^(-n) q⁻¹ =backward shiftoperator.
 5. The method of claim 1 wherein the intelligence control stepis performed according to the algorithm ##EQU11## where T is thelearning sample, and α₁ and α₂ are learning gains.
 6. The method ofclaim 1 wherein the parameter optimization algorithm is ##EQU12## whereT is the learning sample, and α₁ and α₂ are learning gains, and

    u(i)=u(i-T)+β.sub.1 y(i-1)+β.sub.2 [y(i-1)-y(i-2)]

where β₁ and β₂ are fuzzy compensator gains.
 7. The method ofcontrolling and attenuating vibration or noise induced into anautomotive vehicle, comprisingproviding sensors for extracting signalsrelated to vibration and/or acoustic noise from the structure and itsenvirons and actuators for applying attenuating noise and/or vibrationto the structure, providing a microprocessor having inputs from thesensors and outputs to the actuators, performing an on-line modalextraction of vibration and/or noise from the sensors, then operatingthe microprocessor cyclically according to an optimal control algorithmbased on minimum variance, then an intelligence control algorithm, andthen a parameter optimization algorithm, and repeating the modalextraction step when the operations follow the previous cycle are out ofrange, the microprocessor then generating and applying at least onesignal to the actuators which will minimize and/or filter the vibrationor noise introduced to the vehicle.
 8. A system for controlling andattenuating vibration and/or noise in a structure where the vibration isinduced by vibration excitation sources and the noise may resultdirectly or indirectly from the vibrations sources, comprisingaplurality of sensors attached to different parts of the structure forextracting signals related to vibration and/or acoustic noise from thestructure and its environs, a plurality of actuators attached tovibration and/or noise sources on the structure for and capable ofapplying attenuating noise and/or vibration to such sources, amicroprocessor having inputs from said sensors and outputs to saidactuators, said microprocessor also having a memory for storage ofcontrol programs to be operated by said microprocessor, and a pluralityof programs in said memory for use by said microprocessor,said programsincluding a) a first on-line modal extraction program for determiningvibration and/or noise from the sensors, b) a second optimal controlprogram based on a minimum variance algorithm, c) a third program basedon an intelligent control algorithm using stored past performanceinformation, and d) a fourth program based on a fuzzy logic algorithm,the microprocessor running said programs and then generating one or morecontrol signals and applying such signals to said actuators to minimizeand/or filter the vibration or noise introduced to the structure.
 9. Asystem for controlling and attenuating vibration and/or noise in astructure, where the vibration is induced by vibration excitationsources internal and external of the structure and the noise may resultdirectly or indirectly from the vibrations sources, comprisingsensorsattached to parts of the structure for extracting signals related tovibration and/or acoustic noise from the structure and its environs,actuators attached to vibration and/or noise sources on the structurefor applying attenuating noise and/or vibration to such sources, amicroprocessor having inputs from said sensors and outputs to saidactuators, said microprocessor also having a memory for storage ofcontrol programs to be operated by said microprocessor, and an on-linemodal extraction program stored in said memory and used by saidmicroprocessor for comparing vibration and/or noise from the sensors andranking such vibration and/or noise according to its effect uponcomponents of the structure.
 10. A system as defined in claim 9, whereinsaid modal extraction program operates according to the algorithms##EQU13##
 11. A system as defined in claim 9, wherein the second optimalcontrol program is based on a minimum variance algorithm as follows:##EQU14## where y(t)=performance signal at time Tu(t-t₁)=control signalfrom actuator t₁ =time delay caused by the propagation time from the φactuator to the performance signal sensor ζ(t)=random input (e.g. roadvibration) A(q⁻¹)=1+a₁ q⁻¹ +a₂ q⁻² + .... +a_(n) q^(-n), and q⁻¹=backward shift operator.
 12. A system as defined in claim 11, whereinthe third program is based on an intelligence control algorithm asfollows: ##EQU15## where T is the learning sample, and α₁ and α₂ arelearning gains.
 13. A system as defined in claim 12, wherein the fourthprogram is based on a fuzzy logic algorithm as follows: ##EQU16## whereT is the learning sample, and α₁ and α₂ are learning gains, and

    u(i)=u(i-T)+β.sub.1 y(i-1)+β.sub.2 [y(i-1)-y(i-2)]

where β₁ and β₂ are fuzzy compensator gains.
 14. A system forcontrolling and attenuating vibration and/or noise in a structure, wherethe vibration is induced by vibration excitation sources internal andexternal of the structure and the noise may result directly orindirectly from the vibrations sources, comprisingsensors attached toparts of the structure for extracting signals related to vibrationand/or acoustic noise from the structure and its environs, actuatorsattached to vibration and/or noise sources on the structure for applyingattenuating noise and/or vibration to such sources, a microprocessorhaving inputs from said sensors and outputs to said actuators, saidmicroprocessor also having a memory for storage of control programs tobe operated by said microprocessor, said programs including an on-linemodal extraction program stored in said memory and used by saidmicroprocessor for comparing vibration and/or noise from the sensors andranking such vibration and/or noise according to its effect uponcomponents of the structure, a second optimal control program based on aminimum variance algorithm and also stored in said memory, a thirdprogram based on an intelligent control algorithm using stored pastperformance information and also stored in said memory, and a fourthprogram based on a fuzzy logic algorithm and also stored in said memory,the microprocessor running said programs and then generating one or morecontrol signals and applying such signals to said actuators to minimizeand/or filter the vibration or noise according to its effect oncomponents of the structure.
 15. A system as defined in claim 14,whereinsaid microprocessor runs said second, third, and fourth programson a cyclic basis, and wherein said microprossor also runs said modalextraction program whenever the control signals from the previous cycleare out of the system range.